With medical image denoising, multiple methods have been proposed including, Gaussian filtering, wavelet filtering, and non-local means (NLM) algorithms, where experimentation has shown the NLM (possibly combining Wavelet) is the superior method. However, all these methods still share some disadvantages such as the dependency of parameter tuning for different images. In one instance, a proposed method used the redundancy in and relationships of multi-contrast images as a prior for image denoising. Related works have been used to combine a blurry and a noisy pair of images for CMOS sensors and cameras. A further implementation used Group-Sparsity representation for image denoising, which also used the multi-contrast information, but it was not used to advance high SNR contrast to improve the noisier contrast. In another concept relating to the redundancy of multi-contrast images, regularization for compressed sensing reconstruction of undersampled multi-contrast images was demonstrated.
There have been recent developments in deep learning research. Specifically, recent advances in convolutional neural network (CNN) for image recognition with deep residual network, and super-resolution using CNN have shown great promise for improving image resolution. In the recent 5 years, deep learning techniques have advanced the performance of computer vision, specifically in image recognition. The Deep Residual Network (ResNet) approach has been validated as a superior network structure for Convolutional Neural Networks (CNNs) because its by-pass connection helps the performance of CNN. These advances of CNN provide computer vision algorithm super-human capability for recognition. However, it is not clear that the model can be better trained for medical imaging, since there are much fewer data sets available for training, and deep networks typically need thousands or millions of samples due to the number of parameters in the model. Further, it is not clear what network structure is the best for medical images due to the intrinsic properties of medical images in that they are not the same as recognizing common objects within photos. Finally, it is not fully known how to make sure the model does not introduce artifacts that are not in the image or miss the detail of pathology that the model has not seen from the training data.
Super-resolution (SR) CNN methods are used to generate super resolution for images and videos (multi-frame). In one demonstration, with 91 images (from a public benchmark dataset), the SRCNN models can achieve good and similar performance compared with the model trained on large dataset (ImageNet with millions of sub-images). This is because the SRCNN model size (around 10K) is not as large as the model used for other image recognition methods. Further, the training samples the model sees can be counted as smaller local patches, which lead to tens of thousands of patches for the 91 full images. Additionally, the relatively few samples can already capture sufficient variability of natural images patches. SR works try to achieve better performance for aesthetic perception but does not address the need to avoid while preserving details and pathology in for medical images.
Arterial spin labeling (ASL) MRI uses the signal difference between labeled and control image to quantify the blood perfusion. It is a powerful MRI technique and is applied increasingly for research, study, and clinical diagnosis for neurological, cerebrovascular, and psychiatric diseases. However, ASL perfusion maps typically suffer from low SNR due to its signal subtraction. The SNR can be increased if the ASL scans are repeated three or more times for clinics to achieve an acceptable image quality. However this repeating of the scans significantly increases the testing time. Recently proposed multidelay ASL (eASL) can compensate the effect of various transit delays for better sensitivity of perfusion measurement. However, acquiring different delays further increases the time cost and results in even lower SNR and resolution due to the time constraints.
What is needed is a method of image denoising, rather than generating super-resolution, that improves medical images having multi-contrasts.